From Flora to Pharmaceuticals: 100 new additions to angiosperms of Gafargaon subdistrict in Bangladesh and unraveling antidiabetic drug candidates targeting DPP4 through in silico approach

Addition to the angiosperm flora provides essential insights into the biodiversity of a region, contributing to ecological understanding and conservation planning. Gafargaon subdistrict under Mymensingh district in Bangladesh represents a diverse population of angiosperms with a multifaceted ecosystem that demands re-evaluation of the existing angiosperm diversity of Gafargaon to update the status of angiosperm taxa and facilitate their conservation efforts. With this endeavor, a total of 100 angiosperm taxa belonging to 90 genera and 46 families were uncovered as additional occurrence in Gafargaon. The species in the area showcased a variety of life forms, including 63 herbs, 14 shrubs, 14 trees, and 9 climbers. Among the recorded taxa, Chamaecostus cuspidatus (Nees & Mart.) C.D. Specht & D.W. Stev. was selected for antidiabetic drug design endeavor based on citation frequency and ethnomedicinal evidence. A total of 41 phytochemicals of C. cuspidatus were screened virtually, targeting the Dipeptidyl peptidase 4 protein through structure-based drug design approach, which unveiled two lead compounds, such as Tigogenin (-9.0 kcal/mol) and Diosgenin (-8.5 kcal/mol). The lead candidates demonstrated favorable pharmacokinetic and pharmacodynamic properties with no major side effects. Molecular dynamics simulation revealed notable stability and structural compactness of the lead compounds. Principal component analysis and Gibbs free energy landscape further supported the results of molecular dynamics simulation. Molecular mechanics-based MM/GBSA approach unraveled higher free binding energies of Diosgenin (-47.36 kcal/mol) and Tigogenin (-46.70 kcal/mol) over Alogliptin (-46.32 kcal/mol). The outcome of the present investigation would enrich angiosperm flora of Gafargaon and shed light on the role of C. cuspidatus to develop novel antidiabetic therapeutics to combat diabetes.


Introduction
Floristics, the comprehensive study of plant species within a specific region, is fundamental to advancing our current understanding of biodiversity and serves as a cornerstone in conservation biology [1].The continual expansion of a flora's checklist through the addition of new taxa holds significant promise in providing essential baseline data that not only enhances our comprehension of ecosystem dynamics but also aids in the formulation of effective conservation strategies [2].The inclusion of new taxa provides insights into the adaptive capacities of species, contributes to the refinement of knowledge about the distribution and abundance of plant life, and further enables the identification of key areas for conservation intervention [3,4].Gafargaon subdistrict under Mymensingh district in Bangladesh is located in 24˚15' to 243 3'N and 90˚27' to 90˚39'E, spanning an area of 401.16 sq.km.Diverse ecosystems of Gafargaon contribute significantly to the local economy, environment, and primary healthcare system by supporting dense populations of angiosperms [5,6].Regrettably, over the years, human activities have detrimentally impacted the ecosystems of Gafargaon, resulting in the decline of various plant species, some of which are now rare and endangered.Rahman et al. (2019) conducted an investigation on Gafargaon flora and documented 203 taxa belonging to 174 genera and 75 families [5].The study identified several threats, such as habitat degradation, encroachment, over-exploitation of medicinal plants, exotic plantation etc.These challenges necessitate a thorough reassessment of the existing angiosperm diversity of Gafargaon to gauge the status of angiosperm taxa, streamline their preservation efforts and safeguard existing plant species of Gafargaon.
Floristics is instrumental in uncovering medicinal species across diverse plant groups by examining plant diversity and grasping the interconnections among different plant species.Precise species identification becomes imperative before revealing potential drug candidates sourced from plants, and this essential identification process is facilitated by comprehensive taxonomic studies [7].The wealth of plant biodiversity cataloged through floristics, therefore, may serve as a foundation for sourcing potential drug candidates, bridging the gap between traditional botanical knowledge and cutting-edge pharmaceutical research.This interdisciplinary approach harnesses the traditional knowledge encoded in floristics to inform and direct the Structure Based Drug Design (SBDD) strategies, fostering a more comprehensive and sustainable exploration of medicinal resources within natural plant ecosystems [8].
SBDD represents a contemporary computational biology-driven paradigm transforming the drug discovery landscape by expediting processes, curtailing expenses, and expanding research capabilities [9][10][11][12][13].The convergence of floristics and SBDD helps in guiding the progression of antidiabetic drug development.Diabetes is an escalating global concern, affecting approximately 422 million people worldwide and directly contributing to 1.5 million deaths annually.The rising prevalence of diabetes underscores the urgent need for innovative strategies to address this pervasive health issue [https://www.who.int].The Dipeptidyl peptidase 4 (DPP4) enzyme is a key player involved in the regulation of glucose homeostasis [14] and plays a critical role in degrading incretin hormones that stimulate insulin secretion.Inhibiting DPP4 can enhance incretin activity, leading to improved glycemic control in individuals with diabetes [15].Several SBDD studies have used this enzyme as the target protein to propose some bioactive phytochemicals as antidiabetic drug candidates from various angiosperms, such as Pueraria tuberosa, Moringa oleifera, Ocimum tenuiflorum and Amberboa ramosa [16][17][18][19].However, no studies have been reported in silico to denote the antidiabetic potential of Chamaecostus cuspidatus targeting the DPP4 protein.Hence, this ethnomedicinal plant species from our floristics investigation was selected to design novel drug candidates against diabetes.

Ligands preparation.
C. cuspidatus, commonly referred to as the 'Insulin Plant,' is highly esteemed in traditional medicine for its efficacy in managing diabetes.This botanical gem boasts a plethora of bioactive phytochemicals, presenting a promising avenue for the discovery and development of novel antidiabetic drug candidates.Forty-one compounds of C. cuspidatus were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov) after extensive literature survey [34][35][36].Alogliptin, a promising DPP4 inhibitor was used as the control drug and fetched from PubChem [37].All the ligands went through energy minimization process in Open Babel v.2.3.1 and later converted to PDBQT before molecular docking process [38,39].
2.2.3 Active site identification.The target protein was subjected to active site prediction to conduct a site-specific docking.PrankWeb server was utilized for determining active site in default settings [40].The prediction was made by focusing on points positioned on the solvent-accessible surface of proteins.The PDB file of the target protein was uploaded as a custom structure in the PrankWeb server, with the conservation box checked.After generating output, pocket rank, pocket score, confidence level and conservation scores of various output options were analyzed and compared to determine the optimal selection.
2.2.4 Site-specific molecular docking.Prior to conducting site-specific docking, a grid box was generated using AutoDockTools v.1.5.6 incorporating the binding site residues predicted by the PrankWeb server.The size coordinates in the grid box were 54 × 56 × 62, and the center coordinates were 45.589 × -17.354 × -31.654.The grid box covered the active sites of the DPP4 protein.Subsequently, molecular docking analysis was performed with AutoDock Vina [41].The resulting docked complexes were analyzed for molecular interactions via PyMol and BIOVIA Discovery Studio Visualizer v21.1.0.20298 [42].
2.2.5 Drug-likeness evaluation via ADMET.ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics play a crucial role in influencing the pharmacological effects and overall effectiveness of drug candidates, making them vital factors in the selection of lead compounds.SwissADME server was utilized to assess ADME properties of the selected phytochemicals after molecular docking analysis [43].Subsequently, ProToxII and STopTox servers were utilized for toxicity evaluation [44,45].

Molecular dynamics simulation (MDS).
For MDS in GROMACS v.2020.6,ligand topology files were generated using the CgenFF server.All the systems were solvated with the TIP3P (Transferable Intermolecular Potential with 3 Points) water model, employing a triclinic box positioned 1 nm away from the protein surface [46].Neutralization was accomplished by adding sufficient sodium and chloride ions (0.15 M salt).Energy minimization was carried out using the CHARMM36m force field with 5000 steps.In the process of system equilibration and molecular dynamics (MD) simulations, the NVT/NPT ensemble was applied, keeping the pressure and temperature at 1 bar and 300K, respectively.Particle Mesh Ewald (PME) was utilized to calculate long-range interactions.Subsequently, a 100 ns MD production run was executed, targeting approximately 1000 frames per simulation.The time integration step was set to 2fs, and the snapshot interval was configured at 100 ps [42].For trajectory analyses, GROMACS utilities were employed, such as gmx rms, gmx rmsf, gmx gyrate and gmx sasa, which produced trajectory results formatted in CSV (Comma-Separated Values).Subsequently, all the CSV data were visualized using Microsoft Excel v.2013 to assess various parameters including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), and total Solvent Accessible Surface Area (SASA).These analyses were conducted to evaluate the dynamic stability between the DPP4 receptor and potential lead candidates derived from C. cuspidatus.

Principal component analysis.
Principal Component Analysis (PCA) is a widely employed analytical method for depicting the slow and functional motions of biomolecules [47].To obtain the principal components of the protein-ligand complexes, the eigenvalue and eigenvectors of the covariance matrix were calculated and diagonalized.The eigenvectors indicate the direction of motion, while the eigenvalues illustrate both the direction and magnitude of motion.The covariance matrix for PCA was computed for backbone C alpha atoms using the GROMACS analysis tool, gmx covar, which both constructs and diagonalizes the covariance matrix.Additionally, another GROMACS pre-built tool, gmx anaeig, was utilized to assess the overlap between principal components and trajectory coordinates.
2.2.8 Gibbs free energy landscape analysis.The free energy landscape (FEL) serves as a representation of potential conformations assumed by a protein during a molecular dynamics simulation, incorporating Gibbs free energy.The FEL elucidates two variables that capture specific system properties and assess conformational variability.It was generated using the probability distribution derived from the essential plane formed by the first two eigenvectors.The construction of the FEL was carried out using the gmx sham tool.Afterwards, two Python scripts were employed to visualize the results and produce 2D and 3D images.The Python scripts can be found in the S1 File.
2.2.9 MM/GBSA free binding energy.The Prime package of the Schro ¨dinger v.2020-3 software was utilized for MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) calculations [48].The OPLS2005 force field and VSGB continuum solvation model were selected to estimate the free binding energies [49] utilizing the formula: Where, ΔG(solv) denotes variation in GBSA solvation energy between protein-inhibitor complex and the total solvation energies of the unbound inhibitor and protein; ΔE(MM) signifies the discrepancy in minimized energies between the protein-inhibitor complex and total energies of unbound inhibitor and protein; ΔG(SA) represents variation in surface area energies of the protein-inhibitor complex and the aggregated surface area energies of the individual components.
2.2.10 Structural analogs and drug target class.Drug target class was predicted using SwissTargetPrediction server [50].Canonical SMILES was uploaded in the server checking Homo sapiens datasets.Structurally similar analogs were predicted using SwissSimilarity server [51].In target class prediction, the Homo sapiens dataset is prioritized due to its exclusive focus on human proteins.This selection is crucial, especially in pharmaceutical applications, as it ensures that the predicted drug targets are pertinent to human biology.By employing the Homo sapiens dataset, the predictions are customized to human-specific targets, thus enhancing the probability of identifying drug candidates with therapeutic significance in humans.This approach not only increases the likelihood of discovering effective and safe drugs for human use but also ensures that the predictions remain clinically relevant and aligned with human biology.

Taxonomic inventory and enumeration
The current study unveiled the occurrence of an additional 100 species of angiosperms belonging to 90 genera and 46 families within the study area.Among these species, Liliopsida (Monocots) accounted for 23%, while Magnoliopsida (Dicots) constituted 77% of the total.The majority of the species were herbs (63%), followed by shrubs (14%), trees (14%) and climbers (9%) (Fig 1).Within the identified taxa, some ethnomedicinally important species were showcased in Fig 2.

Drug design endeavor
3.2.1 Site-specific molecular docking.The PrankWeb server predicted eight active sites, and the best-ranked site was selected based on pocket and confidence scores.The selected active site demonstrated the highest pocket score (19.23) with a confidence level of 82.8% and encompassed a total of 16 amino acids as binding site residues (Fig 3).The binding site residues were Arg125, Glu205, Glu206, Phe357, Tyr547, Cys551, Lys554, Trp629, Ser630, Tyr631, Val656, Tyr662, Tyr666, Asn710, His740 and Gly741.Molecular docking analysis was successfully conducted for 41 phytochemicals of C. cuspidatus where the binding affinity varied from -5.3 to -10.1 kcal/mol.The highest binding affinity (-10.1 kcal/mol) was recorded for Prosapogenin A, while the lowest (-5.3 kcal/mol) was noted for 4-hydroxy-3-methoxybenzaldehyde (Table 1).Alogliptin scored -7.0 kcal/mol and based on that 53.66% compounds were eliminated and 46.34% compounds were accepted for ADMET analysis.Following ADMET analysis, Tigogenin and Diosgenin emerged as the most promising lead compounds, scoring -9.0 kcal/mol and -8.5 kcal/mol, respectively.Two dimensional structures of all the ligands have been visualized in Fig 4 .3.2.2Molecular interaction analysis.3D visualization of the docked complexes depicted site-specific binding of the best two lead compounds and the control in the active site of the target protein (Fig 5).Protein-ligand interactions unveiled both hydrophobic interactions and conventional hydrogen bonds (Fig 6 and Table 2)

Drug candidacy evaluation via ADMET analysis.
Nineteen phytochemicals scored higher than the control drug in molecular docking analysis and were subsequently subjected to ADMET analysis to evaluated their drug-likeness properties.ADMET analysis revealed Tigogenin and Diosgenin to have the best pharmacokinetic, pharmacodynamic and toxicity properties (Table 3).Tigogenin exhibited a slightly higher molecular weight (416.64 g/mol) than Diosgenin (414.62 g/mol).Molecular weight of Alogliptin was lower (339.39g/mol) than that of both lead compounds.H-bond donating and accepting parameters were very similar for the leads and the control.Molar refractivity was recorded as lower in the control drug compared to both lead compounds.Topological Polar Surface Area (TPSA) was recorded same for Diosgenin and Tigogenin (38.69 Å 2 ), whereas for the control it was slightly higher (97.05Å 2 ).Both Diosgenin and Tigogenin were found to be more lipophilic than Alogliptin.Both the lead compounds and the control exhibited higher gastrointestinal (GI) absorption, and none of the compounds demonstrated inhibitory properties against CYP isoforms, yielding results comparable to the control.Diosgenin was found to be more soluble in water than Tigogenin.In Lipinski's parameters, both Diosgenin and Tigogenin exhibited drug-likeness, each with only one violation.However, when assessed against the Veber and Egan criteria, their drug candidacy was affirmed without any violations.The bioavailability scores were identical for both lead compounds and Alogliptin.Similar to Alogliptin, both Diosgenin and Tigogenin exhibited zero alerts in PAINS test.The synthetic accessibility scores for the two lead compounds were moderate (Table 3).
In the toxicity test, Diosgenin and Tigogenin outperformed the control drug Alogliptin, as evidenced by the results presented in Table 3. Alogliptin exhibited toxicity in the acute oral toxicity criterion, whereas both lead compounds showed no toxic effects.Likewise, Alogliptin demonstrated toxic effects in the eye irritation and corrosion criterion, while the two leads showed no signs of toxicity in this parameter.In all other toxicity parameters, the two leads showcased profiles similar to the control drug (Table 3).

Molecular dynamics simulation (MDS).
The two leads demonstrated dynamic behavior closely paralleled to that of the control drug Alogliptin (Figs 7 and 8).RMSD (Root Mean Square Deviation) analysis, focusing on the backbone after least squares fitting to backbone, revealed that Diosgenin closely tracked Alogliptin from 55 to 100 ns (Fig 7A).Tigogenin showed a close similarity with Diosgenin and Alogliptin between 40 to 48 ns.After 54 ns, Tigogenin displayed a slight elevation and continued up to 100 ns with no drastic fluctuation.The mean RMSD was recorded highest for Tigogenin (1.08 nm), followed by Diosgenin (0.89 nm) and Alogliptin (0.67 nm).
Rg (Radius of Gyration) study showcased very similar behavior for Diosgenin and Tigogenin (Fig 8A ) with mean Rg values of 2.34 and 2.35 nm, respectively.Alogliptin showed initial fluctuations, and after 62 ns, it stabilized, maintaining a steady trajectory until 93 ns.At the 94 th ns, there was a reduction in Rg from 2.53 to 2.31 nm and became stabilized again to 2.52 nm at the 95 th ns.After 95 ns, the trajectory remained consistent up to 100 ns.
SASA (Solvent Accessible Surface Area) analysis revealed very similar results for the two lead compounds and the control drug.Mean SASA values were recorded as 426.06, 429.35 and 427.61 nm 2 for Alogliptin, Diosgenin and Tigogenin, respectively (Fig 8B).Both Diosgenin and Tigogenin followed Alogliptin in close proximity throughout the entire simulation trajectory up to 100 ns.Simulation snapshots supported the structural stability and compactness of the lead compounds after binding with the target protein (Fig 9).

Principal Component Analysis (PCA).
PCA was conducted to explore significant motions during ligand binding in the top two lead-protein complexes and the control-protein complex.Eigenvalues were computed for the initial 20 eigenvectors, with average values of 0.55 nm 2 , 0.84 nm 2 , and 1.76 nm 2 for Alogliptin, Diosgenin, and Tigogenin, respectively (Fig 10A ).Lower trace values of the covariance matrix indicated decreased flexibility and overall stability.The phase space characteristics of the complexes aligned with Fig 10A, where a complex occupying less phase space and forming a stable cluster represented greater stability.Conversely, a complex occupying more space and forming a non-stable cluster indicated lesser stability.The Alogliptin-complex occupied the least phase space, followed by the Diosgenincomplex and Tigogenin-complex.This outcome underscores the superior stability of Diosgenin over Tigogenin compared to the control drug Alogliptin (Fig 10B -10E).
3.2.6Gibbs free energy landscape analysis.FEL (Free energy landscape) study unveiled no single energy minimum for all the three complexes and exhibited multiple energy minima ( Fig 11).All the complexes achieved minimum energy corresponding to their most stable conformations.Smaller and more centralized blue areas depicted greater stability in the complexes.Both Diosgenin and Tigogenin exhibited profile very similar to that of Alogliptin.However, between the two lead compounds, Diosgenin displayed smaller and centralized blue areas than Tigogenin, indicating the superior potential of Diosgenin to induce the target protein to enter a local energy-minimal state.

Discussion
The present investigation documented 100 additional taxa of angiosperms from Gafargaon subdistrict.Among the documented taxa, Magnoliopsida (dicots) were dominant, and the majority of the species were herbs.This finding is consistent with similar other studies where additional taxa of angiosperms were uncovered [52][53][54].Citation frequency was highest for C. cuspidatus regarding its use in treating diabetes; therefore, this species was selected for a drug design endeavor.The selection of this species based on ethnobotanical perception aligns with several other studies where phytochemicals from specific taxa were subjected for drug design based on their ethnomedicinal significance [6,55,56].C. cuspidatus has been reported to possess antidiabetic properties in various studies.Kaur and Mannan (2021) conducted an in vitro investigation using the Bovine Serum Albumin (BSA)-Glucose Assay and found that, after 15 days of incubation, C. cuspidatus extract significantly reduced fluorescence intensity in a concentration-dependent manner.This suggests that the plant extract inhibited the formation of advanced glycation end-products, potentially providing protection against diabetic complications [57].Deogade et al. (2017) performed an in vivo study using C. cuspidatus leaf extract in diabetic rabbits to assess its anti-hyperglycemic activity.The study revealed a notable decrease in blood glucose levels, indicating the antidiabetic potential of C. cuspidatus in an in vivo setting [58].In the present investigation, we employed an in silico strategy to validate previous in vitro and in vivo findings of antidiabetic efficacy of C. cuspidatus targeting the DPP4 protein.
Alogliptin, an FDA-approved DPP4 inhibitor was used as the control drug for virtual Lys554 (2.72, 2.81) 2 Arg125 (1.04) , Phe357 (4.19) , Trp627 (4.82) , Trp629 (4.92, 5.27) , His740 (3.50)   -9.0 Diosgenin Arg125, Tyr547, Trp627, Trp629 Arg125 (2.13, 2.34)  2 Tyr547 (4.10, 5.40) , Trp627 (5.49) , Trp629 (5.15)   -8.5 Alogliptin Arg125, Tyr547, Trp629, Tyr666, His740 Arg125 (2.20, 2.21) , Trp629 (2.46) , Tyr666 (2.76)   4 Tyr547 (4.20, 4.94) , Trp629 (3.99, 4.93,  5.60) His740 (3.59)   -7.0 https://doi.org/10.1371/journal.pone.0301348.t002screening of phytocompounds through molecular docking analysis.Alogliptin improves glucose control in diabetes by prolonging the action of incretin hormone.Additionally, Alogliptin boosts insulin secretion, reduces glucagon production, and lowers blood sugar levels after meals [31].The virtual screening revealed that 46.34% of phytocompounds scored higher than Alogliptin (-7.0 kcal/mol) with binding affinity ranging from -7.2 to -10.1 kcal/mol.).The study demonstrated a binding affinity ranging from -6.0 to -9.4 kcal/mol for the investigated phytochemicals of M. olerifera [59].In our study, both the upper and lower thresholds of the binding affinity of the top selected compounds were higher, providing justification for the antidiabetic efficacy of C. cuspidatus phytocompounds from an energetic perspective.Molecular interaction analysis revealed that the best two lead compounds, Tigogenin and Diosgenin, interacted with more than two active site residues (Table 2).Both the lead compounds and the control drug showed interaction with Arg125 and Trp629.This suggests that these two residues are likely catalytically important sites, serving as potential drug surface hotspots and playing a significant role in the drug's mechanism of action.ADMET analysis revealed the drug-likeness of Tigogenin and Diosgenin, and our result was found to be consistent with previous studies [11,60].Lipinski parameters are essential in the development of potential lead candidates.Tigogenin and Diosgenin demonstrated favorable outcomes in Lipinski parameters, consistent with findings from previous SBDD investigation [11,61].Forecasting CYP isoform inhibition stands as a pivotal stage in ADMET investigations.This assessment is crucial for gauging the potential outcomes of drug candidates, confirming their safety and therapeutic efficacy [61].In the present study, Tigogenin and Diosgenin displayed CYP inhibition tendency very similar to Alogliptin.In toxicity parameter, these two lead compounds showed better results than the control drug, further justifying their selection as potential inhibitors of DPP4.The PAINS (Pan-assay interference   8).RMSD, RMSF, Rg and SASA profiles of the two leads closely resembled those of Alogliptin.The similar dynamic behavior of the two leads corroborated their selection as potential antidiabetic drug candidates.Simulation trajectories were consistent with findings from previous studies using GROMACS software [13,60].PCA, also   referred to as covariance analysis or essential dynamics (ED), is a systematic approach for delineating the collective and comprehensive movements within a protein system by methodically reducing the complexity of the system [62].PCA analysis showcased similar dynamic motion for both the leads and the control.However, among the two leads, Diosgenin was found to be more stable than Tigogenin ( Fig 10).The results from Gibbs Free Energy Landscape (FEL) supported the findings of PCA, indicating better stability of Diosgenin over Tigogenin ( Fig 11).Both the leads and the control drug exhibited multiple energy minima, suggesting the existence of multiple stable states or configurations within the systems.The FEL and PCA analyses unraveled that the binding of Tigogenin and Diosgenin not only modified the conformation of DPP4 protein but also altered the essential dynamics for inhibition.The PCA and FEL assessments were consistent with findings from other studies [63,64].The MM/ GBSA free binding energy was found to be higher in Diosgenin and Tigogenin than that of Alogliptin, which corroborated the docking protocol by eliminating the possibility of falsepositive results.This implies that Diosgenin and Tigogenin are likely to form strong and stabilizing interactions with DPP4 upon binding, potentially leading to a favorable pharmacological response.Ahmed et al. (2022) proposed three antidiabetic lead compounds from Piper betle phytochemicals targeting alpha-amylase and alpha-glucosidase proteins.In that study, MM/ GBSA free binding energy, estimated using Prime module of Schro ¨dinger, ranged from -34.66 to -45.02 kcal/mol for the three leads against alpha-amylase protein.In the case of alpha-glucosidase, the binding energy spanned from -28.68 to -38.28 kcal/mol for the same three lead compounds of P. betle [65].In our study, the binding energy varied from -46.70 to -47.36 kcal/ mol, showing better results compared to the P. betle phytocompounds (Table 5).The prediction of drug targets may assist in identifying novel targets for Diosgenin and Tigogenin, while forecasting structurally analogous compounds streamlines the drug design process targeting the DPP4 protein [20].The current study employed the SBDD approach to identify and validate potential inhibitors against the DPP4 protein; however, certain limitations are associated with this method.The computer-based tools used in the present study are valuable, though not always perfectly accurate.Consequently, the precise efficacy of Tigogenin and Diosgenin in hindering the target protein in vitro and in vivo remains uncertain, requiring further wet-lab validations.Additionally, SBDD methods encounter challenges in various aspects, including benchmarking, constrained prediction approaches, and a lack of diverse datasets for different computational analyses [66].Despite these challenges, the current investigation has the potential to shed light on the role of C. cuspidatus in uncovering antidiabetic therapeutics.

Conclusion
In the present investigation, an interdisciplinary approach was employed to document additional angiosperm taxa in Gafargaon subdistrict and to guide drug design process from the floristics survey.The taxonomic inventory unveiled a total of 100 additional taxa of angiosperms under 90 genera and 46 families.Documenting the presence of these additional taxa will enrich the flora of Gafargaon and contribute to a deeper understanding of angiosperm diversity in this subdistrict.This would illuminate key aspects of conservation planning by identifying and safeguarding the plant species within Gafargaon, aiding in the formulation of targeted conservation strategies.Documentation of additional taxa also holds potential medicinal importance, uncovering new plant resources that could be explored for pharmaceutical applications and highlighting the interconnectedness of biodiversity, human health, and sustainable resource utilization.Ethnobotanical knowledge regarding the antidiabetic use of C. cuspidatus has been validated in computational framework employing a comprehensive SBDD protocol.Tigogenin and Diosgenin, identified as potential inhibitors against the DPP4 protein, demonstrated favorable pharmacokinetic and pharmacodynamic properties with no major side effects.These compounds showed noteworthy structural stability in a 100 ns simulation, and the MM/GBSA approach further corroborated their selection.The proposed lead candidates, Tigogenin and Diosgenin unveiled from the SBDD study hold promise for the development of novel inhibitors targeting the DPP4 protein to combat diabetes.In future, the efficacy of the lead candidates will be investigated through multiple in vitro enzymatic assays, encompassing the Dipeptidyl peptidase-4 inhibition assay, Alpha-amylase inhibition assay, and Aldose reductase inhibition assay.Favorable outcomes will guide subsequent in vivo investigations using animal models.Additionally, long-term endeavors will focus on fostering extensive collaborative research for development of novel antidiabetic therapeutics.

3. 2 . 8
Structural analogs and drug target class.Tigogenin was predicted to interact with 7 distinct drug target classes, as illustrated in Fig 12A.Enzymes emerged as the most prevalent target class, constituting 26.7% of the predictions.In the case of Diosgenin, predictions spanned 11 different target classes, with nuclear receptors being the most frequently predicted class, accounting for 20% of the interactions (Fig 12B).

Fig 3 .
Fig 3. Binding cavity identification.A. Surface view showing active site inside the red circle, B. Ribbon view indicating active site inside the red circle.https://doi.org/10.1371/journal.pone.0301348.g003

Fig 4 .
Fig 4. Two dimensional structures of all the phytochemicals and control drug Alogliptin used for molecular docking analysis.The number of compounds is in accordance with the serial number enlisted in Table1.

Fig 5 .
Fig 5. Docked complexes with three dimensional molecular interactions between the lead compounds and target protein.A. Interaction of Tigogenin.B. Interaction of Diosgenin.C. Interaction of Alogliptin.D. The two lead candidates and control are superimposed at the active site of DPP4 protein.https://doi.org/10.1371/journal.pone.0301348.g005

Fig 7 .
Fig 7. Simulation trajectory of the lead candidates and control drug for 100 ns. A. RMSD focusing on backbone after least squares fitting to backbone.B. RMSD focusing on the ligand after least square fitting to protein.C. RMSF analysis.https://doi.org/10.1371/journal.pone.0301348.g007

Fig 10 .
Fig 10.Principle component analysis showing eigenvalue versus eigenvector index and projection of the motion in phase space for the lead compounds and control drug.https://doi.org/10.1371/journal.pone.0301348.g010